Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking.

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms.

Evaluating Bacterial Nanocellulose Interfaces for Recording Surface Biopotentials from Plants.

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes.

Adolescent Asthma Monitoring: A Preliminary Study of Audio and Spirometry Modalities.

Asthma patients' sleep quality is correlated with how well their asthma symptoms are controlled. In this paper, deep learning techniques are explored to improve forecasting of forced expiratory volume in one second (FEV1) by using audio data from participants and test whether auditory sleep disturbances are correlated with poorer asthma outcomes. These are applied to a representative data set of FEV1 collected from a commercially available sprirometer and audio spectrograms collected overnight using a smartphone.

A Wearable System for Continuous Monitoring and Assessment of Speech, Gait, and Cognitive Decline for Early Diagnosis of ADRD.

Early detection of cognitive decline is essential to study mild cognitive impairment and Alzheimer's Disease in order to develop targeted interventions and prevent or stop the progression of dementia. This requires continuous and longitudinal assessment and tracking of the related physiological and behavioral changes during daily life. In this paper, we present a low cost and low power wearable system custom designed to track the trends in speech, gait, and cognitive stress while also considering the important human factor needs such as privacy and compliance.

Efficient imaging and computer vision detection of two cell shapes in young cotton fibers.

Premise: The shape of young cotton () fibers varies within and between commercial cotton species, as revealed by previous detailed analyses of one cultivar of and one of . Both narrow and wide fibers exist in cv. Deltapine 90, which may impact the quality of our most abundant renewable textile material.

Static posturography as a novel measure of the effects of aging on postural control in dogs.

Aging is associated with impairment in postural control in humans. While dogs are a powerful model for the study of aging, the associations between age and postural control in this species have not yet been elucidated. The aims of this work were to establish a reliable protocol to measure center of pressure excursions in standing dogs and to determine age-related changes in postural sway.

Fusion of Human Gaze and Machine Vision for Predicting Intended Locomotion Mode.

Predicting the user's intended locomotion mode is critical for wearable robot control to assist the user's seamless transitions when walking on changing terrains. Although machine vision has recently proven to be a promising tool in identifying upcoming terrains in the travel path, existing approaches are limited to environment perception rather than human intent recognition that is essential for coordinated wearable robot operation. Hence, in this study, we aim to develop a novel system that fuses the human gaze (representing user intent) and machine vision (capturing environmental information) for accurate prediction of the user's locomotion mode.

Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices.

Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording.

Simultaneous Localization of Biobotic Insects using Inertial Data and Encounter Information.

Several recent research efforts have shown that the bioelectrical stimulation of their neuro-mechanical system can control the locomotion of Madagascar hissing cockroaches (Gromphadorhina portentosa). This has opened the possibility of using these insects to explore centimeter-scale environments, such as rubble piles in urban disaster areas. We present an inertial navigation system based on machine learning modules that is capable of localizing groups of G.

Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals.

Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope.

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units.

Disaster robotics is a growing field that is concerned with the design and development of robots for disaster response and disaster recovery. These robots assist first responders by performing tasks that are impractical or impossible for humans. Unfortunately, current disaster robots usually lack the maneuverability to efficiently traverse these areas, which often necessitate extreme navigational capabilities, such as centimeter-scale clearance.

Reliable Vision-Based Grasping Target Recognition for Upper Limb Prostheses.

Computer vision has shown promising potential in wearable robotics applications (e.g., human grasping target prediction and context understanding).

A Feasibility Study of a Wearable Real-Time Notification System for Self-Awareness of Body-Rocking Behavior.

Wearable sensors have been shown to be effective for promoting self-awareness, wellness and re-education. In this work, we perform a preliminary study analyzing the real-time detection and annotation of body-rocking behavior in individuals, which is a type of Stereotypical Motor Movement (SMM). We develop a platform for real-time annotation and detection using wireless inertial sensors and an embedded device.

Gaze Fixation Comparisons Between Amputees and Able-bodied Individuals in Approaching Stairs and Level-ground Transitions: A Pilot Study.

This paper aims to investigate the visual strategy of transtibial amputees while they are approaching the transition between level-ground and stairs and compare it with that of able-bodied individuals. To this end, we conducted a pilot study where two transtibial amputee subjects and two able-bodied subjects transitioned from level-ground to stairs and vice versa while wearing eye tracking glasses to record gaze fixations. To investigate how vision functioned to both populations for preparing locomotion on new terrains, gaze fixation behavior before the new terrains were analyzed and compared between two populations across all transition cases in the study.

Signal Quality for RR Interval Prediction on Wearable Sensors.

Physiological responses are essential for health monitoring. Wearable devices are providing greater populations of people with the ability to monitor physiological signals during their day to day activities. However, wearable devices are particularly susceptible to degradation of signal quality due to noise from motion artifacts, environment, and user error.

Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses.

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices.

A Framework for Physiological Response Prediction with Joint Activity State optimization.

Physiological responses are essential for health monitoring. However, modeling the complex interactions be- tween them across activity and environmental factors can be challenging. In this paper, we introduce a framework that identifies the state of an individual based on their activity, trains predictive models for their physiological response within these states, and jointly optimizes for the states and the models.

Visual Terrain Identification and Surface Inclination Estimation for Improving Human Locomotion with a Lower-Limb Prosthetic.

Lower-limb robotic prosthetics can benefit from context awareness to provide comfort and safety to the amputee. In this work, we developed a terrain identification and surface inclination estimation system for a prosthetic leg using visual and inertial sensors. We built a dataset from which images with high sharpness are selected using the IMU signal.

Prediction of Physiological Response over Varying Forecast Lengths with a Wearable Health Monitoring Platform.

The goal of this study is to characterize the accuracy of prediction of physiological responses for varying forecast lengths using multi-modal data streams from wearable health monitoring platforms. We specifically focus on predicting breathing rate due to its significance in medical and exercise physiology research. We implement a nonlinear support vector machine regression model for accurate prediction of future values of these physiological signals with forecast windows of up to one minute long.

Comparing wearable devices with wet and textile electrodes for activity recognition.

This paper explores the idea of identifying activities from muscle activation which is captured by wearable ECG recording devices that use wet and textile electrodes. Most of the devices available today filter out the high frequency components to retain only the signal related to an ECG. We explain how the high frequency components that correspond to muscle activation can be extracted from the recorded signal and can be used to identify activities.

Preliminary statistical assessment towards characterization of biobotic control.

Biobotic research involving neurostimulation of instrumented insects to control their locomotion is finding potential as an alternative solution towards development of centimeter-scale distributed swarm robotics. To improve the reliability of biobotic agents, their control mechanism needs to be precisely characterized. To achieve this goal, this paper presents our initial efforts for statistical assessment of the angular response of roach biobots to the applied bioelectrical stimulus.

A LAG FUNCTIONAL LINEAR MODEL FOR PREDICTION OF MAGNETIZATION TRANSFER RATIO IN MULTIPLE SCLEROSIS LESIONS.

We propose a lag functional linear model to predict a response using multiple functional predictors observed at discrete grids with noise. Two procedures are proposed to estimate the regression parameter functions: (1) an approach that ensures smoothness for each value of time using generalized cross-validation; and (2) a global smoothing approach using a restricted maximum likelihood framework. Numerical studies are presented to analyze predictive accuracy in many realistic scenarios.

Continuous Shape Estimation of Continuum Robots Using X-ray Images.

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images).

Planning Curvature-Constrained Paths to Multiple Goals Using Circle Sampling.

We present a new sampling-based method for planning optimal, collision-free, curvature-constrained paths for nonholonomic robots to visit multiple goals in any order. Rather than sampling configurations as in standard sampling-based planners, we construct a roadmap by sampling circles of constant curvature and then generating feasible transitions between the sampled circles. We provide a closed-form formula for connecting the sampled circles in 2D and generalize the approach to 3D workspaces.